Deeptech proof

Evidence-led operating intelligence for restaurant service.

SavorQ's deeptech story starts with a live decision substrate: routing decisions, outcomes, labels, snapshots, model registry records, and experiment scaffolding that can be inspected before adaptive learning claims are made.

See value model

Operator-reviewed AIChannel economicsKitchen-ready workflow

Reviewed insight layer

OrdersDemand and mixSavorQ VoiceCall, transcript, and review signalMenu COGSCost baselineChannel feesContribution signalKDS timingExecution signalOperator reviewCorrection loop

Investor proof surface

The proof is the learning loop already present in the platform.

GET /api/decisions/proof summarizes the tenant and store evidence behind the claim: decisions made, outcomes observed, labeled examples prepared, station snapshots computed, models registered, and experiments available. It does not present closed-loop ML as already live.

RoutingDecision rowsDecision log

OutcomeEvent rowsOutcome stream

Labeled TrainingExample rowsTraining set

StationPerformanceSnapshot windowsSnapshot score

DeployedModel recordsModel registry

ABTestExperiment recordsExperiment layer

Learning loop ladder

From operational decisions to pilot-gated adaptive intelligence.

Current capability is the substrate: decisions, outcomes, training rows, snapshots, model registry, fine-tune preparation, and experiment scaffolding. Wave 3 learning requires enough pilot data and reviewed model performance.

Decide

A tenant/store-scoped routing decision captures station choice, reasoning, and input features.

Observe

Outcome events attach margin, completion, reroute, and service evidence to the decision record.

Label

Training examples materialize success, failure, or unknown labels from settled outcomes.

Improve

Fine-tune preparation, model registry, and experiment scaffolding support Wave 2 and Wave 3 validation.

Claim tiers

Every deeptech claim is assigned to live substrate, Wave 2 readiness, or roadmap.

The investor narrative is intentionally restrained: SavorQ can show the data exhaust and preparation path today, while autonomous adaptive learning remains a Wave 3 outcome.

Live substrate

Routing decisions, outcome events, training rows, station snapshots, model registry records, and experiment scaffolding can be evidenced from the decision service.

Wave 2 ready

Fine-tune preparation becomes credible when enough labeled success and failure examples exist for a tenant and store.

Roadmap

Closed-loop adaptive learning remains gated on pilot data, active model deployments, experiment results, and operator review.

Operating graph

Restaurant intelligence improves when service evidence is connected.

The deeptech angle is not generic AI. It is the accumulation of operational evidence: demand, kitchen execution, economics, review events, and store comparisons that can support better routing and margin decisions once validated.

POS, online, marketplace, and phoneDemand

KDS timing, station pressure, and handoffExecution

Fees, COGS, modifiers, refunds, paymentsEconomics

Stock pressure, waste risk, cost driftInventory

Manager approvals and correction loopsReview

Store, channel, and menu comparisonsBenchmarking

Guardrails

Demo-safe deeptech positioning with proof before claims.

Wave 3 is described as roadmap in this V1. Future promotion requires the data, active model, experiment evidence, and operator review needed to support that tier.

Evidence-led claims

Each investor claim maps to live substrate, Wave 2 readiness, or roadmap language.

Tenant-scoped proof

The proof endpoint summarizes one tenant and store from existing decision-service tables.

Human review stays explicit

Recommendations and experiment outcomes remain reviewable before operational reliance.

Data moat path is staged

The moat grows through decisions, outcomes, labels, snapshots, and validated pilot feedback.

Investor demo